Semantic Range Image for Loop Closure of 3D Lidar SLAM

The purpose of SLAM loop closure detection is to reduce the cumulative error in the system by identifying the same scene that the robot passed through. Traditional loop detection methods detect loops based on physical information. These methods use basic physical features to identify loop closures and pay less attention to the role of semantic information as high-level information in loop closure detection. This paper regards semantic information as one of the criteria for confirming loop closure, so that semantic information can be deeply involved in the process of loop closure detection. We construct a global descriptor based on range image and semantic information, which is called SeRI. The descriptor encodes the semantic and geometric features of the surrounding environment. Loop closure candidates are determined by matching between pairs of semantic range images. We evaluate our method on the KITTI dataset. Experiments have shown that the proposed method can effectively identify loop closure candidates correctly with high precision and recall rate.